Overview

Dataset statistics

Number of variables18
Number of observations12330
Missing cells0
Missing cells (%)0.0%
Duplicate rows72
Duplicate rows (%)0.6%
Total size in memory1.7 MiB
Average record size in memory144.0 B

Variable types

Numeric14
Categorical2
Boolean2

Warnings

Dataset has 72 (0.6%) duplicate rowsDuplicates
ProductRelated_Duration is highly correlated with Informational_Duration and 3 other fieldsHigh correlation
OperatingSystems is highly correlated with Browser and 1 other fieldsHigh correlation
Browser is highly correlated with OperatingSystems and 2 other fieldsHigh correlation
TrafficType is highly correlated with BrowserHigh correlation
Informational_Duration is highly correlated with ProductRelated_Duration and 3 other fieldsHigh correlation
Informational is highly correlated with ProductRelated_Duration and 2 other fieldsHigh correlation
ProductRelated is highly correlated with ProductRelated_Duration and 4 other fieldsHigh correlation
VisitorType is highly correlated with OperatingSystems and 1 other fieldsHigh correlation
Administrative is highly correlated with ProductRelatedHigh correlation
ExitRates is highly correlated with BounceRatesHigh correlation
Administrative_Duration is highly correlated with ProductRelated_Duration and 2 other fieldsHigh correlation
BounceRates is highly correlated with ExitRatesHigh correlation
Administrative has 5822 (47.2%) zeros Zeros
Administrative_Duration has 5962 (48.4%) zeros Zeros
Informational has 9737 (79.0%) zeros Zeros
Informational_Duration has 9936 (80.6%) zeros Zeros
ProductRelated_Duration has 886 (7.2%) zeros Zeros
BounceRates has 5583 (45.3%) zeros Zeros
PageValues has 9630 (78.1%) zeros Zeros
SpecialDay has 11098 (90.0%) zeros Zeros

Reproduction

Analysis started2021-09-20 23:37:32.618508
Analysis finished2021-09-20 23:38:08.040269
Duration35.42 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Administrative
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.294160584
Minimum0
Maximum27
Zeros5822
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:08.136886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.311939233
Coefficient of variation (CV)1.4436388
Kurtosis4.761141897
Mean2.294160584
Median Absolute Deviation (MAD)1
Skewness1.971903348
Sum28287
Variance10.96894148
MonotonicityNot monotonic
2021-09-21T01:38:08.283918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
05822
47.2%
11345
 
10.9%
21105
 
9.0%
3909
 
7.4%
4758
 
6.1%
5567
 
4.6%
6429
 
3.5%
7336
 
2.7%
8286
 
2.3%
9223
 
1.8%
Other values (17)550
 
4.5%
ValueCountFrequency (%)
05822
47.2%
11345
 
10.9%
21105
 
9.0%
3909
 
7.4%
4758
 
6.1%
5567
 
4.6%
6429
 
3.5%
7336
 
2.7%
8286
 
2.3%
9223
 
1.8%
ValueCountFrequency (%)
271
 
< 0.1%
261
 
< 0.1%
244
 
< 0.1%
233
 
< 0.1%
224
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
196
 
< 0.1%
1811
0.1%
1716
0.1%

Administrative_Duration
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3313
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.05360237
Minimum0
Maximum3398.75
Zeros5962
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:08.441954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q391.8125
95-th percentile345.925
Maximum3398.75
Range3398.75
Interquartile range (IQR)91.8125

Descriptive statistics

Standard deviation176.0633061
Coefficient of variation (CV)2.199317718
Kurtosis51.18070004
Mean80.05360237
Median Absolute Deviation (MAD)6
Skewness5.644288656
Sum987060.9172
Variance30998.28775
MonotonicityNot monotonic
2021-09-21T01:38:08.619983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05962
48.4%
456
 
0.5%
553
 
0.4%
745
 
0.4%
640
 
0.3%
1140
 
0.3%
1437
 
0.3%
935
 
0.3%
1533
 
0.3%
1032
 
0.3%
Other values (3303)5997
48.6%
ValueCountFrequency (%)
05962
48.4%
1.3333333331
 
< 0.1%
215
 
0.1%
326
 
0.2%
3.54
 
< 0.1%
456
 
0.5%
4.3333333331
 
< 0.1%
4.52
 
< 0.1%
4.751
 
< 0.1%
553
 
0.4%
ValueCountFrequency (%)
3398.751
< 0.1%
2720.51
< 0.1%
2657.3180561
< 0.1%
2629.2539681
< 0.1%
2407.423811
< 0.1%
2156.1666671
< 0.1%
2137.1127451
< 0.1%
2086.751
< 0.1%
2047.2348481
< 0.1%
1951.2791411
< 0.1%

Informational
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.497242498
Minimum0
Maximum24
Zeros9737
Zeros (%)79.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:08.776030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.265488237
Coefficient of variation (CV)2.545012228
Kurtosis27.39252069
Mean0.497242498
Median Absolute Deviation (MAD)0
Skewness4.07480477
Sum6131
Variance1.601460479
MonotonicityNot monotonic
2021-09-21T01:38:08.910045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
09737
79.0%
11023
 
8.3%
2719
 
5.8%
3374
 
3.0%
4221
 
1.8%
595
 
0.8%
678
 
0.6%
736
 
0.3%
915
 
0.1%
814
 
0.1%
Other values (7)18
 
0.1%
ValueCountFrequency (%)
09737
79.0%
11023
 
8.3%
2719
 
5.8%
3374
 
3.0%
4221
 
1.8%
595
 
0.8%
678
 
0.6%
736
 
0.3%
814
 
0.1%
915
 
0.1%
ValueCountFrequency (%)
241
 
< 0.1%
161
 
< 0.1%
142
 
< 0.1%
131
 
< 0.1%
125
 
< 0.1%
111
 
< 0.1%
107
 
0.1%
915
0.1%
814
 
0.1%
736
0.3%

Informational_Duration
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1255
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.37813029
Minimum0
Maximum2549.375
Zeros9936
Zeros (%)80.6%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:09.059797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile194.775
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.6837643
Coefficient of variation (CV)4.092245945
Kurtosis76.47961598
Mean34.37813029
Median Absolute Deviation (MAD)0
Skewness7.589505513
Sum423882.3464
Variance19791.92152
MonotonicityNot monotonic
2021-09-21T01:38:09.243825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09936
80.6%
932
 
0.3%
726
 
0.2%
626
 
0.2%
1025
 
0.2%
1323
 
0.2%
1223
 
0.2%
822
 
0.2%
1121
 
0.2%
1621
 
0.2%
Other values (1245)2175
 
17.6%
ValueCountFrequency (%)
09936
80.6%
13
 
< 0.1%
1.51
 
< 0.1%
211
 
0.1%
2.51
 
< 0.1%
316
 
0.1%
3.51
 
< 0.1%
417
 
0.1%
518
 
0.1%
5.53
 
< 0.1%
ValueCountFrequency (%)
2549.3751
< 0.1%
2256.9166671
< 0.1%
2252.0333331
< 0.1%
2195.31
< 0.1%
2166.51
< 0.1%
2050.4333331
< 0.1%
1949.1666671
< 0.1%
1830.51
< 0.1%
1779.1666671
< 0.1%
17781
< 0.1%

ProductRelated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct310
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.47956204
Minimum0
Maximum705
Zeros37
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:09.421216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q337
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)30

Descriptive statistics

Standard deviation44.47929593
Coefficient of variation (CV)1.412957902
Kurtosis31.26814218
Mean31.47956204
Median Absolute Deviation (MAD)13
Skewness4.346476479
Sum388143
Variance1978.407766
MonotonicityNot monotonic
2021-09-21T01:38:09.596268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1747
 
6.1%
2458
 
3.7%
3453
 
3.7%
4401
 
3.3%
6389
 
3.2%
7386
 
3.1%
5376
 
3.0%
8365
 
3.0%
10326
 
2.6%
9314
 
2.5%
Other values (300)8115
65.8%
ValueCountFrequency (%)
037
 
0.3%
1747
6.1%
2458
3.7%
3453
3.7%
4401
3.3%
5376
3.0%
6389
3.2%
7386
3.1%
8365
3.0%
9314
2.5%
ValueCountFrequency (%)
7051
< 0.1%
6861
< 0.1%
5841
< 0.1%
5341
< 0.1%
5181
< 0.1%
5171
< 0.1%
5011
< 0.1%
4861
< 0.1%
4701
< 0.1%
4491
< 0.1%

ProductRelated_Duration
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9450
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1178.780128
Minimum0
Maximum63973.52223
Zeros886
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:09.774334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1174.5833333
median586.6916667
Q31445.797348
95-th percentile4269.849833
Maximum63973.52223
Range63973.52223
Interquartile range (IQR)1271.214015

Descriptive statistics

Standard deviation1899.140849
Coefficient of variation (CV)1.611106944
Kurtosis140.5636989
Mean1178.780128
Median Absolute Deviation (MAD)498.6916667
Skewness7.325042995
Sum14534358.97
Variance3606735.963
MonotonicityNot monotonic
2021-09-21T01:38:09.946367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0886
 
7.2%
1721
 
0.2%
1117
 
0.1%
817
 
0.1%
1516
 
0.1%
1215
 
0.1%
1915
 
0.1%
2214
 
0.1%
1314
 
0.1%
513
 
0.1%
Other values (9440)11302
91.7%
ValueCountFrequency (%)
0886
7.2%
0.51
 
< 0.1%
12
 
< 0.1%
2.3333333331
 
< 0.1%
2.6666666671
 
< 0.1%
35
 
< 0.1%
410
 
0.1%
513
 
0.1%
5.3333333331
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
63973.522231
< 0.1%
43171.233381
< 0.1%
29970.465971
< 0.1%
27009.859431
< 0.1%
24844.15621
< 0.1%
23888.811
< 0.1%
23342.082051
< 0.1%
23050.104141
< 0.1%
21857.046481
< 0.1%
18504.126211
< 0.1%

BounceRates
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1859
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02202550489
Minimum0
Maximum0.2
Zeros5583
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:10.126913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.002898551
Q30.016666667
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.016666667

Descriptive statistics

Standard deviation0.04839063907
Coefficient of variation (CV)2.19702746
Kurtosis7.79077013
Mean0.02202550489
Median Absolute Deviation (MAD)0.002898551
Skewness2.958928276
Sum271.5744753
Variance0.00234165395
MonotonicityNot monotonic
2021-09-21T01:38:10.315974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05583
45.3%
0.2696
 
5.6%
0.066666667133
 
1.1%
0.028571429114
 
0.9%
0.05112
 
0.9%
0.025100
 
0.8%
0.03333333398
 
0.8%
0.01666666798
 
0.8%
0.197
 
0.8%
0.0493
 
0.8%
Other values (1849)5206
42.2%
ValueCountFrequency (%)
05583
45.3%
2.73 × 10-51
 
< 0.1%
3.35 × 10-51
 
< 0.1%
3.83 × 10-51
 
< 0.1%
3.94 × 10-51
 
< 0.1%
7.09 × 10-51
 
< 0.1%
7.27 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8.01 × 10-51
 
< 0.1%
8.08 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.2696
5.6%
0.1833333331
 
< 0.1%
0.185
 
< 0.1%
0.1769230771
 
< 0.1%
0.1751
 
< 0.1%
0.1666666674
 
< 0.1%
0.1642857141
 
< 0.1%
0.1642307691
 
< 0.1%
0.1619047621
 
< 0.1%
0.163
 
< 0.1%

ExitRates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4739
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04460261209
Minimum0
Maximum0.2
Zeros74
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:10.619821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0046371831
Q10.014285714
median0.0256394085
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.05073922355
Coefficient of variation (CV)1.137584127
Kurtosis3.472928937
Mean0.04460261209
Median Absolute Deviation (MAD)0.0143605915
Skewness2.064342652
Sum549.9502071
Variance0.002574468806
MonotonicityNot monotonic
2021-09-21T01:38:10.803863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2821
 
6.7%
0.1335
 
2.7%
0.05323
 
2.6%
0.033333333290
 
2.4%
0.066666667266
 
2.2%
0.025220
 
1.8%
0.04213
 
1.7%
0.016666667180
 
1.5%
0.02164
 
1.3%
0.022222222150
 
1.2%
Other values (4729)9368
76.0%
ValueCountFrequency (%)
074
0.6%
0.0001755931
 
< 0.1%
0.0002504381
 
< 0.1%
0.0002621231
 
< 0.1%
0.0002631581
 
< 0.1%
0.0002923981
 
< 0.1%
0.0004098361
 
< 0.1%
0.0004464291
 
< 0.1%
0.0004683841
 
< 0.1%
0.0004807691
 
< 0.1%
ValueCountFrequency (%)
0.2821
6.7%
0.1923076921
 
< 0.1%
0.1888888892
 
< 0.1%
0.1866666674
 
< 0.1%
0.1833333332
 
< 0.1%
0.1818181821
 
< 0.1%
0.180341881
 
< 0.1%
0.183
 
< 0.1%
0.1777777785
 
< 0.1%
0.1756
 
< 0.1%

PageValues
Real number (ℝ≥0)

ZEROS

Distinct2674
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.817762304
Minimum0
Maximum361.7637419
Zeros9630
Zeros (%)78.1%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:10.989918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.65165768
Maximum361.7637419
Range361.7637419
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.47196969
Coefficient of variation (CV)3.175098727
Kurtosis66.80896392
Mean5.817762304
Median Absolute Deviation (MAD)0
Skewness6.439459291
Sum71733.00921
Variance341.2136642
MonotonicityNot monotonic
2021-09-21T01:38:11.161957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09630
78.1%
53.9886
 
< 0.1%
42.293067523
 
< 0.1%
12.558857142
 
< 0.1%
14.12736982
 
< 0.1%
54.982
 
< 0.1%
34.039975362
 
< 0.1%
58.92417662
 
< 0.1%
44.893459372
 
< 0.1%
10.999018442
 
< 0.1%
Other values (2664)2677
 
21.7%
ValueCountFrequency (%)
09630
78.1%
0.0380345421
 
< 0.1%
0.0670495461
 
< 0.1%
0.0935469491
 
< 0.1%
0.0986214031
 
< 0.1%
0.1206999141
 
< 0.1%
0.1296768931
 
< 0.1%
0.1318370131
 
< 0.1%
0.1392006231
 
< 0.1%
0.1506504981
 
< 0.1%
ValueCountFrequency (%)
361.76374191
< 0.1%
360.95338391
< 0.1%
287.95379281
< 0.1%
270.78469311
< 0.1%
261.49128571
< 0.1%
258.54987321
< 0.1%
255.56915791
< 0.1%
254.60715791
< 0.1%
246.75859021
< 0.1%
239.981
< 0.1%

SpecialDay
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0603730738
Minimum0
Maximum1
Zeros11098
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:11.313138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1971691711
Coefficient of variation (CV)3.265846158
Kurtosis10.14819349
Mean0.0603730738
Median Absolute Deviation (MAD)0
Skewness3.335685077
Sum744.4
Variance0.03887568204
MonotonicityNot monotonic
2021-09-21T01:38:11.441167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
011098
90.0%
0.6345
 
2.8%
0.8321
 
2.6%
0.4241
 
2.0%
0.2176
 
1.4%
1149
 
1.2%
ValueCountFrequency (%)
011098
90.0%
0.2176
 
1.4%
0.4241
 
2.0%
0.6345
 
2.8%
0.8321
 
2.6%
1149
 
1.2%
ValueCountFrequency (%)
1149
 
1.2%
0.8321
 
2.6%
0.6345
 
2.8%
0.4241
 
2.0%
0.2176
 
1.4%
011098
90.0%

Month
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
May
3447 
Nov
2969 
Mar
1889 
Dec
1710 
Oct
545 
Other values (5)
1770 

Length

Max length4
Median length3
Mean length3.023114355
Min length3

Characters and Unicode

Total characters37275
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeb
2nd rowFeb
3rd rowFeb
4th rowFeb
5th rowFeb

Common Values

ValueCountFrequency (%)
May3447
28.0%
Nov2969
24.1%
Mar1889
15.3%
Dec1710
13.9%
Oct545
 
4.4%
Sep445
 
3.6%
Aug431
 
3.5%
Jul428
 
3.5%
June285
 
2.3%
Feb181
 
1.5%

Length

2021-09-21T01:38:11.712277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-21T01:38:11.819298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
may3447
28.0%
nov2969
24.1%
mar1889
15.3%
dec1710
13.9%
oct545
 
4.4%
sep445
 
3.6%
aug431
 
3.5%
jul428
 
3.5%
june285
 
2.3%
feb181
 
1.5%

Most occurring characters

ValueCountFrequency (%)
M5336
14.3%
a5336
14.3%
y3447
9.2%
N2969
8.0%
o2969
8.0%
v2969
8.0%
e2621
7.0%
c2255
 
6.0%
r1889
 
5.1%
D1710
 
4.6%
Other values (12)5774
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24945
66.9%
Uppercase Letter12330
33.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a5336
21.4%
y3447
13.8%
o2969
11.9%
v2969
11.9%
e2621
10.5%
c2255
9.0%
r1889
 
7.6%
u1144
 
4.6%
t545
 
2.2%
p445
 
1.8%
Other values (4)1325
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
M5336
43.3%
N2969
24.1%
D1710
 
13.9%
J713
 
5.8%
O545
 
4.4%
S445
 
3.6%
A431
 
3.5%
F181
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin37275
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M5336
14.3%
a5336
14.3%
y3447
9.2%
N2969
8.0%
o2969
8.0%
v2969
8.0%
e2621
7.0%
c2255
 
6.0%
r1889
 
5.1%
D1710
 
4.6%
Other values (12)5774
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII37275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M5336
14.3%
a5336
14.3%
y3447
9.2%
N2969
8.0%
o2969
8.0%
v2969
8.0%
e2621
7.0%
c2255
 
6.0%
r1889
 
5.1%
D1710
 
4.6%
Other values (12)5774
15.5%

OperatingSystems
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.123682076
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:11.947318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9078913789
Coefficient of variation (CV)0.4275081421
Kurtosis10.64815984
Mean2.123682076
Median Absolute Deviation (MAD)0
Skewness2.089678156
Sum26185
Variance0.8242667559
MonotonicityNot monotonic
2021-09-21T01:38:12.071860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
26661
54.0%
12554
 
20.7%
32532
 
20.5%
4472
 
3.8%
879
 
0.6%
619
 
0.2%
77
 
0.1%
56
 
< 0.1%
ValueCountFrequency (%)
12554
 
20.7%
26661
54.0%
32532
 
20.5%
4472
 
3.8%
56
 
< 0.1%
619
 
0.2%
77
 
0.1%
879
 
0.6%
ValueCountFrequency (%)
879
 
0.6%
77
 
0.1%
619
 
0.2%
56
 
< 0.1%
4472
 
3.8%
32532
 
20.5%
26661
54.0%
12554
 
20.7%

Browser
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.355068938
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:12.195879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.708867869
Coefficient of variation (CV)0.7256126739
Kurtosis12.87455361
Mean2.355068938
Median Absolute Deviation (MAD)0
Skewness3.256636983
Sum29038
Variance2.920229393
MonotonicityNot monotonic
2021-09-21T01:38:12.331924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
28006
64.9%
12431
 
19.7%
4731
 
5.9%
5463
 
3.8%
6174
 
1.4%
10161
 
1.3%
8134
 
1.1%
3104
 
0.8%
1360
 
0.5%
749
 
0.4%
Other values (3)17
 
0.1%
ValueCountFrequency (%)
12431
 
19.7%
28006
64.9%
3104
 
0.8%
4731
 
5.9%
5463
 
3.8%
6174
 
1.4%
749
 
0.4%
8134
 
1.1%
91
 
< 0.1%
10161
 
1.3%
ValueCountFrequency (%)
1360
 
0.5%
1210
 
0.1%
116
 
< 0.1%
10161
 
1.3%
91
 
< 0.1%
8134
 
1.1%
749
 
0.4%
6174
 
1.4%
5463
3.8%
4731
5.9%

Region
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.124736415
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:12.454954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.398599021
Coefficient of variation (CV)0.767616433
Kurtosis-0.1246812208
Mean3.124736415
Median Absolute Deviation (MAD)2
Skewness0.9962871157
Sum38528
Variance5.753277264
MonotonicityNot monotonic
2021-09-21T01:38:12.574979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
14859
39.4%
32373
19.2%
41170
 
9.5%
21127
 
9.1%
6798
 
6.5%
7755
 
6.1%
9504
 
4.1%
8429
 
3.5%
5315
 
2.6%
ValueCountFrequency (%)
14859
39.4%
21127
 
9.1%
32373
19.2%
41170
 
9.5%
5315
 
2.6%
6798
 
6.5%
7755
 
6.1%
8429
 
3.5%
9504
 
4.1%
ValueCountFrequency (%)
9504
 
4.1%
8429
 
3.5%
7755
 
6.1%
6798
 
6.5%
5315
 
2.6%
41170
 
9.5%
32373
19.2%
21127
 
9.1%
14859
39.4%

TrafficType
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.044120032
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2021-09-21T01:38:12.709174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.012087329
Coefficient of variation (CV)0.9920791906
Kurtosis3.584535946
Mean4.044120032
Median Absolute Deviation (MAD)1
Skewness1.986428472
Sum49864
Variance16.09684473
MonotonicityNot monotonic
2021-09-21T01:38:12.846394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
24020
32.6%
12426
19.7%
32020
16.4%
41054
 
8.5%
13725
 
5.9%
10444
 
3.6%
6441
 
3.6%
8336
 
2.7%
5257
 
2.1%
11245
 
2.0%
Other values (10)362
 
2.9%
ValueCountFrequency (%)
12426
19.7%
24020
32.6%
32020
16.4%
41054
 
8.5%
5257
 
2.1%
6441
 
3.6%
739
 
0.3%
8336
 
2.7%
942
 
0.3%
10444
 
3.6%
ValueCountFrequency (%)
20198
 
1.6%
1917
 
0.1%
1810
 
0.1%
171
 
< 0.1%
163
 
< 0.1%
1538
 
0.3%
1413
 
0.1%
13725
5.9%
121
 
< 0.1%
11245
 
2.0%

VisitorType
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
Returning_Visitor
10563 
New_Visitor
1682 
Other
 
85

Length

Max length17
Median length17
Mean length16.09878345
Min length5

Characters and Unicode

Total characters198498
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturning_Visitor
2nd rowReturning_Visitor
3rd rowReturning_Visitor
4th rowReturning_Visitor
5th rowReturning_Visitor

Common Values

ValueCountFrequency (%)
Returning_Visitor10563
85.7%
New_Visitor1682
 
13.6%
Other85
 
0.7%

Length

2021-09-21T01:38:13.133478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-21T01:38:13.230500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
returning_visitor10563
85.7%
new_visitor1682
 
13.6%
other85
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i35053
17.7%
t22893
11.5%
r22893
11.5%
n21126
10.6%
e12330
 
6.2%
_12245
 
6.2%
V12245
 
6.2%
s12245
 
6.2%
o12245
 
6.2%
R10563
 
5.3%
Other values (6)24660
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter161678
81.5%
Uppercase Letter24575
 
12.4%
Connector Punctuation12245
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i35053
21.7%
t22893
14.2%
r22893
14.2%
n21126
13.1%
e12330
 
7.6%
s12245
 
7.6%
o12245
 
7.6%
u10563
 
6.5%
g10563
 
6.5%
w1682
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
V12245
49.8%
R10563
43.0%
N1682
 
6.8%
O85
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_12245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin186253
93.8%
Common12245
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i35053
18.8%
t22893
12.3%
r22893
12.3%
n21126
11.3%
e12330
 
6.6%
V12245
 
6.6%
s12245
 
6.6%
o12245
 
6.6%
R10563
 
5.7%
u10563
 
5.7%
Other values (5)14097
7.6%
Common
ValueCountFrequency (%)
_12245
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII198498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i35053
17.7%
t22893
11.5%
r22893
11.5%
n21126
10.6%
e12330
 
6.2%
_12245
 
6.2%
V12245
 
6.2%
s12245
 
6.2%
o12245
 
6.2%
R10563
 
5.3%
Other values (6)24660
12.4%

Weekend
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
False
9486 
True
2844 
ValueCountFrequency (%)
False9486
76.9%
True2844
 
23.1%
2021-09-21T01:38:13.410541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Revenue
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
False
10422 
True
1908 
ValueCountFrequency (%)
False10422
84.5%
True1908
 
15.5%
2021-09-21T01:38:13.465554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2021-09-21T01:37:35.374470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:35.468492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:35.569515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:35.713052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:35.820077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:35.943104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:36.051127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:36.160152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:36.291910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:36.453940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:36.604983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:36.866046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:37.014066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:37.171110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:37.324151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:37.480922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:37.644975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:37.803009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:37.963781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:38.127826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:38.290855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:38.455892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:38.619928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:38.778970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:38.938009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:39.101126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:39.254148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:39.417197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:39.580235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:39.723255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:39.875300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:40.021405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:40.167441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:40.332490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:40.487533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:40.753031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:40.910070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:41.065100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:41.213124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:41.368160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:41.516194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:41.671229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:41.824350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:41.971385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:42.123410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:42.269454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:42.414483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:42.567512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:42.718553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:42.881115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:43.038138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:43.187961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:43.337997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:43.491029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:43.636063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:43.793097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:43.949133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:44.107168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:44.272205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:44.534334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:44.689384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:44.856901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:45.016455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:45.183493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:45.354902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:45.513944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:45.669072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:45.829279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:45.984308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:46.147350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:46.315388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:46.468423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:46.629459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:46.780486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:46.936522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:47.096559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:47.254189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:47.416239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:47.577274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:47.737305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:47.889339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:48.048765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:48.305824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:48.468862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:48.628903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:48.788932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:48.961965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:49.133001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:49.299052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:49.468488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:49.635527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:49.805572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:49.977617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:50.146649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:50.311735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:50.478776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:50.637493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:50.805531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:50.972568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:51.134611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:51.300255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:51.458290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:51.619135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:51.785169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:51.952193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:52.230269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:52.401920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:52.566956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:52.727834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:52.895876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:53.059507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:53.230549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:53.398584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:53.551621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:53.710655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:53.862689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:54.018262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:54.177297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:54.337334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:54.501365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:54.663407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:54.821437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:54.976464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:55.136501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:55.294017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:55.460056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:55.622735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:55.766772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:56.029850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:56.175879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:56.324588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:56.483637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:56.636659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:56.792322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:56.949358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:57.104874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:57.258908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:57.414944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:57.563978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:57.723021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:57.879062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:58.036301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:58.202339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:58.354689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:58.514723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:58.677760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:58.836803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:59.002839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:59.170872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:59.331914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:59.484950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:59.650138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:37:59.922950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:00.091629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:00.269670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:00.419703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:00.578615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:00.723643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:00.872670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:01.027706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:01.182394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:01.337424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:01.491464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:01.639342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:01.787382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:01.938409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:02.086435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:02.240673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:02.394720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:02.557753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:02.726795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:02.890519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:03.054555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:03.219606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:03.385630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:03.557196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:03.833783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:03.995812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:04.157568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:04.322772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:04.477813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:04.642837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:04.805886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:04.961544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:05.127581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:05.286332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:05.450361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:05.615321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:05.780383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:05.947264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:06.115295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:06.284188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:06.440227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:06.614666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:06.768701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-21T01:38:06.933737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-21T01:38:13.601614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-21T01:38:13.863672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-21T01:38:07.278816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-21T01:38:07.811507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
00.00.00.00.01.00.00.20.20.00.0Feb1.01.01.01.0Returning_VisitorFalseFalse
10.00.00.00.02.064.00.00.10.00.0Feb2.02.01.02.0Returning_VisitorFalseFalse
20.00.00.00.01.00.00.00.20.00.0Feb4.01.09.03.0Returning_VisitorFalseFalse
30.00.00.00.02.02.6666670.050.140.00.0Feb3.02.02.04.0Returning_VisitorFalseFalse
40.00.00.00.010.0627.50.020.050.00.0Feb3.03.01.04.0Returning_VisitorTrueFalse
50.00.00.00.019.0154.2166670.0157890.0245610.00.0Feb2.02.01.03.0Returning_VisitorFalseFalse
60.00.00.00.01.00.00.20.20.00.4Feb2.04.03.03.0Returning_VisitorFalseFalse
71.00.00.00.00.00.00.20.20.00.0Feb1.02.01.05.0Returning_VisitorTrueFalse
80.00.00.00.02.037.00.00.10.00.8Feb2.02.02.03.0Returning_VisitorFalseFalse
90.00.00.00.03.0738.00.00.0222220.00.4Feb2.04.01.02.0Returning_VisitorFalseFalse

Last rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
123200.00.00.00.08.0143.5833330.0142860.050.00.0Nov2.02.03.01.0Returning_VisitorFalseFalse
123210.00.00.00.06.00.00.20.20.00.0Nov1.08.04.01.0Returning_VisitorFalseFalse
123226.076.250.00.022.01075.250.00.0041670.00.0Dec2.02.04.02.0Returning_VisitorFalseFalse
123232.064.750.00.044.01157.976190.00.0139530.00.0Nov2.02.01.010.0Returning_VisitorFalseFalse
123240.00.01.00.016.0503.00.00.0376470.00.0Nov2.02.01.01.0Returning_VisitorFalseFalse
123253.0145.00.00.053.01783.7916670.0071430.02903112.2417170.0Dec4.06.01.01.0Returning_VisitorTrueFalse
123260.00.00.00.05.0465.750.00.0213330.00.0Nov3.02.01.08.0Returning_VisitorTrueFalse
123270.00.00.00.06.0184.250.0833330.0866670.00.0Nov3.02.01.013.0Returning_VisitorTrueFalse
123284.075.00.00.015.0346.00.00.0210530.00.0Nov2.02.03.011.0Returning_VisitorFalseFalse
123290.00.00.00.03.021.250.00.0666670.00.0Nov3.02.01.02.0New_VisitorTrueFalse

Duplicate rows

Most frequently occurring

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue# duplicates
230.00.00.00.01.00.00.20.20.00.0Mar2.02.01.01.0Returning_VisitorFalseFalse15
320.00.00.00.01.00.00.20.20.00.0Mar3.02.03.01.0Returning_VisitorFalseFalse8
390.00.00.00.01.00.00.20.20.00.0May2.02.01.03.0Returning_VisitorFalseFalse7
340.00.00.00.01.00.00.20.20.00.0May1.01.01.03.0Returning_VisitorFalseFalse6
120.00.00.00.01.00.00.20.20.00.0Dec8.013.09.020.0OtherFalseFalse5
280.00.00.00.01.00.00.20.20.00.0Mar2.04.01.01.0Returning_VisitorFalseFalse4
370.00.00.00.01.00.00.20.20.00.0May1.01.04.03.0Returning_VisitorFalseFalse4
20.00.00.00.01.00.00.20.20.00.0Dec1.01.04.01.0Returning_VisitorTrueFalse3
40.00.00.00.01.00.00.20.20.00.0Dec2.02.01.01.0Returning_VisitorFalseFalse3
60.00.00.00.01.00.00.20.20.00.0Dec2.02.01.013.0Returning_VisitorFalseFalse3